License plate recognition with low-rank, shared character classifiers

Abstract

A method is disclosed for performing multiple classification of an image simultaneously using multiple classifiers, where information between the classifiers is shared explicitly and is achieved with a low-rank decomposition of the classifier weights. The method includes applying an input image to classifiers and, more particularly, multiplying the extracted input image features by |Σ| embedding matrices Ŵc to generate a latent representation of d-dimensions for each of the |Σ| characters. The embedding matrices are uncorrelated with a position of the extracted character. The step of applying the extracted character to the classifiers further includes projecting the latent representation with a decoding matrix shared by all the character embedding matrices to generate scores of every character in an alphabet at every position. At least one of the multiplying the extracted input image features and the projecting the latent representation with the decoding matrix are performed with a processor.

Description

BACKGROUND[0001]The present disclosure is directed to low-rank, shared character classifiers. It finds particular application in conjunction with license plate recognition (LPR), and will be described with particular reference thereto. However, it is to be appreciated that the present exemplary embodiment is also amenable to other like applications, such as general text recognition in images.[0002]Currently, convolutional neural networks (“deep convolutional network”, “CNNs”, “NNs” or “ConvNets”) can be used to perform lexicon-free text recognition. FIG. 1 shows an example of the architecture of a character CNN used for license plate recognition in the PRIOR ART. In the existing CNN, convolving of an input image with learned filters generates a stack of activations. Each stack of activations can undergo additional convolutions with more filters to generate a new stack. In one embodiment, the activations can be further fed through a series of fully-connected layers to produce more ac...

AI Technical Summary

Benefits of technology

[0010]In one embodiment of the disclosure, a method is disclosed for performing multiple classification of an image simultaneously using multiple classifiers, where information between the classifiers is shared explicitly and is achieved with a low-rank decomposition of the classifier weights. The method includes acquiring an input image and extracting a feature representation from the input image. The method includes applying the extracted feature representation to classifiers. The step of applying the extracted feature representation to the classifiers includes multiplying the extracted feature representation by |Σ| embedding matrices Ŵc to generate a latent representation of d-dimensions for each of the |Σ| characters, where |Σ| denotes the size of Σ. The embedding matrices are uncorrelated with a position of the extracted character. The step of applying the extracted feature representation to the classifiers further includes projecting the latent representation with a decoding matrix shared by all the character embedding to generate scores of every character in an alphabet at every position. At least one of the multiplying the extracted feature representation from the input image and the projecting the latent representation with the decoding matrix are performed with a processor.

Problems solved by technology

However, one disadvantage of CNNs is the large amounts of data needed to effectively train the CNN, which makes them difficult to use for the task of license plate recognition.

However, it still requires several thousand annotated license plates for training the CNN.

Thus, there exists a challenge in obtaining a sizable sample of annotated license plate images where every possible combination of character-position pairs appears multiple times in the dataset of sample images.

Images